Most mentioned countries (world and by continent)

Libraries

pacman::p_load(haven,
               ggplot2,
               tidyverse,
               viridis, 
               plotly)

Data

setwd("~/Documents/GitHub/Final_Data_Science_Project/News source df's")
dat <- read_csv("data_set_full.csv") %>% 
  select(-INCOME_GRP, -count, -orientation) %>% 
  mutate(rate = as.numeric(rate)) %>% 
  rename(Country = ADMIN2) 


dat_top_rates <-  dat %>% 
  group_by(Country) %>% 
  summarise(tot_rate = sum(rate))

dat <- left_join(dat, dat_top_rates) %>% 
  arrange(desc(tot_rate)) 

World

dat_top_10_world <- dat %>% 
  head(126) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

personal_theme = theme(plot.title =
                         element_text(hjust = 0.5))

plot_world <- dat_top_10_world %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5)  +
  ggtitle("10 most mentioned countries in the world") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", 
       fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme


ggplotly(plot_world)

Asia

dat_top_10_asia <- dat %>% 
  filter(CONTINENT == "Asia", Country != "Oman") %>% 
  head(115) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

plot_asia <- dat_top_10_asia %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned Asian countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_asia)

Africa

dat_top_10_africa <- dat %>% 
  filter(CONTINENT == "Africa") %>% 
  head(111) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

plot_africa <- dat_top_10_africa %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned African countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_africa)

Europe

dat_top_10_europe <- dat %>% 
  filter(CONTINENT == "Europe") %>% 
  head(117) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

plot_europe <- dat_top_10_europe %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned European countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_europe)

South America

dat_top_10_samerica <- dat %>% 
  filter(CONTINENT == "South America") %>% 
  head(71) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

plot_samerica <- dat_top_10_samerica %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned South American countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_samerica)

North America

dat_top_10_namerica <- dat %>% 
  filter(CONTINENT == "North America") %>% 
  head(95) %>% 
  mutate(Country = fct_reorder(Country, tot_rate))

plot_namerica <- dat_top_10_namerica %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned North American countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_namerica)

10 most mentioned countries in Oceania

dat_top_10_oceania <- dat %>% 
  filter(CONTINENT == "Oceania") %>% 
  head(75) %>% 
  mutate(Country = fct_reorder(Country, tot_rate)) 

plot_oceania<- dat_top_10_oceania %>% 
  ggplot(aes(x = Country, y = rate, fill = source)) + 
  geom_col(color = "black", size = 0.2, width = .5) +
  ggtitle("10 most mentioned Oceanian countries") +
  theme_minimal() +
  labs(y ="Proportion of country mentions on total headlines (%)", fill = "Source") + 
  scale_fill_viridis(option ="plasma", discrete = T) + 
  coord_flip() + 
  personal_theme

ggplotly(plot_oceania)